This paper addresses the challenge of classifying and assigning programming tasks to experts, a process that typically requires significant effort, time, and cost. To tackle this issue, a novel dataset containing a total of 4,112 programming tasks was created by extracting tasks from various websites. Web scraping techniques were employed to collect this dataset of programming problems systematically. Specific HTML tags were tracked to extract key elements of each issue, including the title, problem description, input-output, examples, problem class, and complexity score. Examples from the dataset are provided in the appendix to illustrate the variety and complexity of tasks included. The dataset's effectiveness has been evaluated and benchmarked using two approaches; the first approach involved fine-tuning the FLAN-T5 small model on the dataset, while the second approach used in-context learning (ICL) with the GPT-4o mini. The performance was assessed using standard metrics: accuracy, recall, precision, and F1-score. The results indicated that in-context learning with GPT-4o-mini outperformed the FLAN-T5 model.

本研究解决了将编程任务分类和分配给专家的挑战，该过程通常需要大量的时间和成本。通过网络爬虫技术创建了一个新的包含4,112个编程任务的数据集，系统性地提取了任务的关键要素。结果显示，基于上下文学习的GPT-4o-mini在性能上优于FLAN-T5模型，展示了该数据集在任务分配中的潜力。

任务复杂性：用于任务复杂性分类的数据集及其在In-Context Learning、FLAN-T5和GPT-4o基准测试中的应用